Textual spatial correlation quantifies the degree of association between knowledge described in text and specific geographic spaces. For example, the statement ‘the nutria is an invasive species’ is valid only in certain regions. In geographic information science, measuring textual spatial correlation is not only a fundamental prerequisite for spatiotemporal computing, but also a cornerstone for high-precision geo-artificial intelligence applications. To address the limitations of annotation dependency and low computational efficiency in the existing methods, this study proposed a Spatial Correlation Index and corresponding calculation method. This method fully leverages implicit spatial knowledge from a large-scale knowledge base and through a semantic-matching mechanism, enabling the efficient calculation of textual spatial correlation. The effectiveness of the method was evaluated based on the spatial correlation calculations of textual data and entities in a knowledge graph. Results demonstrate that the proposed method achieves performance comparable to few-shot prompted GPT-4.1 and DeepSeek-V3, while outperforming their zero-shot counterparts, with average F1-score improvements of 18.65% and 27.27%, respectively. Meanwhile, the proposed method reduces the average calculation time by 77.37% and 77.74%, respectively. This study achieved a breakthrough in efficiently measuring textual spatial correlations, thereby providing an essential technical foundation for spatial-related intelligent computing.
Qiu et al. (Sat,) studied this question.
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